In this article, a novel DC–DC converter with high voltage gain capability is presented. The proposed converter is synthesised from (i) a basic two‐phase interleaved boost converter (IBC) which uses coupled inductors (CIs) instead of discrete inductors and (ii) diode‐capacitor multiplier (DCM) cells to achieve higher voltage conversion ratio. The outputs obtained from the interleaved and the DCM stages are judiciously cascaded with the outputs obtained from the secondary winding of the CIs to enhance the voltage gain. The input current is almost ripple free due to the adopted interleaving mechanism. As voltage gain is extended using CIs and DCMs, the voltage stress on the semiconductor devices is minimal and only a fraction (10.5%) of the output voltage. Experimental results obtained from the 18 V/380 V, 150 W prototype converter, operating at a maximum efficiency of 94% under full‐load condition, validate the proposed concept. Further, practical results obtained under closed‐loop condition confirm that the converter yields a constant output of 380 V DC which is suitable for microgrid application.

This study proposes a novel non‐isolated high‐gain high‐power DC–DC converter. The converter is developed from a basic high‐gain high‐power converter structure. The proposed converter comprises of a three‐phase interleaved boost converter, one coupled inductor and a voltage multiplier cell which act as gain extension stage. The main desirable features like voltage gain, stress on the switches and diodes, turns ratio, component utilisation factor and efficiency are summarised. The power circuit, operational details, simulation and experimental results of the 60 V/1.1 kV, 3 kW, 100 kHz proposed converter are presented.

This article presents the selection of an appropriate deep learning Long Short-Term Memory (LSTM) based probabilistic hour-ahead forecasting model for a grid connected industrial solar PV power plant located in Poland. It has a 317 kW peak power capacity and is connected with a metallurgical plant producing steel for car parts. The purpose of the study is to present a model that could be used by the plant to participate in the Polish intra-day electricity market. Four different LSTM models were investigated which include the vanilla model, the stacked LSTM model, the Bi-directional LSTM model and the LSTM-Autoencoder model. Out of the investigated models it was observed that the LSTM-Autoencoder model was the best performing one in terms of reliability. The average Root Mean Squared Error (RMSE) and the average Mean Absolute Error (MAE) for the Autoencoder model over 100 runs were 15.59 kW and 8.36 kW which represent 4.9% and 2.6% of the peak power respectively. Moreover, it was observed that it has the shortest width for the 95% confidence interval of only 0.5% for both the RMSE and the MAE. In terms of accuracy the best performing model was the LSTM bi-directional model with the average RMSE and MAE values of 12.87 kW and 6.91 kW which represent 4% and 2.1% of the peak power. The 95% confidence intervals width for both the RMSE and MAE over the 100 runs were 0.8% and 0.5% respectively.

The electrical energy demand is growing every day. Fossil fuel-based electrical power generation pollutes the environment. So, to fulfil the electrical energy demand, clean renewable energy sources like solar and wind are emerging as viable alternatives. But the amount of power generated from solar and wind energy sources is varying in magnitude and is intermittent in nature. So, predicting solar and wind power generation is indispensable for efficient planning and controlling of a renewable energy system. This paper aims to identify a machine learning model suitable for time-series prediction of wind power and also surveys the predominantly existing machine learning followed by the deep learning approaches. The main objective of this study is to solve a real-life problem in the renewable energy sector by accurately estimating the amount of power generation production per hour by applying machine learning techniques using historical wind power energy production data. Hence, historical wind data set with input parameters like wind speed, wind direction air temperature, pressure, and output parameters of wind power generation is considered for training and testing using mean absolute error for evaluating the prediction accuracy. Layers from simple to complex deep layers are trained and tested for prediction accuracy, i.e. baseline, linear, dense, multidense, convolution neural networks, and long short-term memory. Investigating further, LSTM and Residual LSTM prove to have higher mean absolute prediction accuracy of 0.0987 and 0.0958. This eventually enlightens the wind power forecasting models and new paths for further research applications.